Support Vector Machines for Optimizing Speaker Recognition Problems.

Abstract: Classi cation of data has many applications, amongst others within the eld of speaker recognition. Speaker recognition is the part of speech processing concerned with the task of automatically identifying or verifying speakers using dierent characteristics of their voices. The main focus in speaker recognition is to nd methods that separate data, in order to dierentiate between dierent speakers. In this thesis, such a method is obtained by building a support vector machine, which has proved to be a very good tool for separating all kinds of data. The rst version of the support vector machine is used to separate linearly separable data using linear hyperplanes, and it is then modi ed to separate linearly non-separable data, by allowing some data points to be misclassi ed. Finally, the support vector machine is improved further, through a generalization to higher dimensional data and by the use of dierent kernels and thus higher order hyperplanes. The developed support vector machine is in the end used on a set of speaker recognition data. The separation of two speakers are not very satisfying, most likely due to the very limited set of data. However, the results are very good when the support vector machine is used on other, more complete, sets of data.